Multiple-input And Multiple-output Systems Research Articles

Control Structure Design Using Global Sensitivity Analysis for Mineral Processes under Uncertainties

Multiple-input and multiple-output (MIMO) systems can be found in many industrial processes, including mining processes. In practice, these systems are difficult to control due to the interactions of their input variables and the inherent uncertainty of industrial processes. Depending on the interactions in the MIMO process, different control strategies can be implemented to achieve the desired performance. Among these strategies is the use of a decentralized structure that considers several subsystems and for which a SISO controller can be designed. In this study, a methodology based on global sensitivity analysis (GSA) to design decentralized control structures for industrial processes under uncertainty is presented. GSA has not yet been applied for this purpose in process control; it allows us to understand the dynamic behavior of systems under uncertainty in a broad value range, unlike approaches proposed in the literature. The proposed GSA is based on the Sobol method, which provides sensitivity indices used as interaction measures to establish the input–output pairing for MIMO systems. Two case studies based on a semi-autogenous grinding (SAG) mill and a solvent extraction (SX) plant are presented to demonstrate the applicability of the proposed methodology. The results indicate that the methodology allows the design of 2 × 2 and 3 × 3 decentralized control structures for the SAG mill and SX plant, respectively, which exhibit good performance compared to MPC. For example, for the SAG mill, the determined pairings were fresh ore flux/fraction of mill filling and power consumption/percentage of critical speed.

Design of multiple-input and multiple-output antenna for modern wireless applications

In this paper, multiple-input and multiple-output (MIMO) antennas are designed and simulated. The designed antennas are compact double-sided printed microstrip patch antennas and fed by a microstrip line. These antennas are designed for 3.5 to 10 GHz frequencies used for medical, industrial, sciences, and various fields of 5G communications and networking applications. Furthermore, a MIMO system is designed using the polarization variability of the individual antennas, which yields better results in terms of mutual coupling (S12 and S21), reflection coefficient (S11 and S22), and voltage standing wave ratio (VSWR), which is less than 2 indicate improved matching conditions. The designed antennas showed an acceptable gain (around 2 dB) and an envelope correlation coefficient (ECC) is <0.002. In addition, the proposed MIMO antennas exhibited isolation is -25 dB at 6 GHz, which is preferable in 5G mobile antennas.

Разработка и анализ системы с множеством излучателей и множеством приёмников для исследовательских целей

Systems with multiple input and multiple output (MIMO) play a key role in modern wireless communication devices. MIMO are used in wireless networks to increase channel throughput and transmission reliability. The implementation of the MIMO system will speed up research in the field of improving communication systems. In this article, we present a system-level analysis of a block level analog RF receiver in a MIMO system in accordance with the performance requirements described in the 3GPP Long-Term Evolution standard. An analytical study of the analog receiving part was carried out. The results show that the proposed system architecture satisfies the given requirements with a good margin. Numerical simulation of the operation of the 44 MIMO system in the line-of sight environments was performed. Taking into account the influence of only the noise of the receiver, a curve is obtained that determines the dependence of the error probability on the distance between the transmitter and receiver. Using this curve, it is possible to determine the distance corresponding to a given error probability for given aperture sizes of the transmitting and receiving arrays.

Adaptive MIMO Detector Based on Hypernetwork: Design, Simulation, and Experimental Test

Algorithm unfolding, which provides a systematic connection between conventional model-based algorithms and modern data-based deep learning, has exhibited great empirical success for efficiently balancing the performance and complexity of multiple-input and multiple-output (MIMO) detectors. However, existing unfolding-based MIMO detectors have difficulties adapting to the high discrepancy in channel and noise conditions. In this study, we present a novel unfolding-based framework for MIMO detectors, which can automatically determine internal parameters of an unfolding-based MIMO detector to adapt to the varying conditions. A key part of our approach is to develop a hypernetwork that can effectively learn to generate the internal parameters in the sophisticated expectation propagation-based MIMO detector. In particular, we design long short-term memory-based hypernetwork to ensure the flexibility of the layers of the unfolded algorithm. The proposed framework is also extended to a coded MIMO turbo receiver to adapt to the different feedback beliefs from the decoder. Numerical results demonstrate that the proposed MIMO detectors have excellent adaptation capability to different channel environments and noise levels. Compared with the existing unfolded algorithm that is an optimal reference, the proposed framework avoids frequent retraining and presents the nearly optimal performance in uncoded and coded MIMO systems. An over-the-air platform is presented as well to demonstrate the significant robustness of the proposed receivers in practical deployment.

Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems.

The current wireless communication infrastructure has to face exponential development in mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems and their variants (i.e., Multi-User MIMO and Massive MIMO) are the most promising 5G wireless communication systems technology due to their high system throughput and data rate. However, the most significant challenges in MIMO communication are substantial problems in exploiting the multiple-antenna and computational complexity. The recent success of RL and DL introduces novel and powerful tools that mitigate issues in MIMO communication systems. This article focuses on RL and DL techniques for MIMO systems by presenting a comprehensive review on the integration between the two areas. We first briefly provide the necessary background to RL, DL, and MIMO. Second, potential RL and DL applications for different MIMO issues, such as detection, classification, and compression; channel estimation; positioning, sensing, and localization; CSI acquisition and feedback, security, and robustness; mmWave communication and resource allocation, are presented.

Modelling and Stability Analysis for a Magnetically Levitated Slice Motor (MLSM) with Gyroscopic Effect and Non-Collocated Structure Based on the Extended Inverse Nyquist Stability Criterion

Stability of the rotor motion is the precondition for the reliable operation of magnetically levitated slice motors (MLSMs). However, with gyroscopic effect and non-collocated structure existing simultaneously, its stability analysis faces a tremendous challenge, because the torsional motions couple with the radial translational ones, making MLSM a multiple-input and multiple-output (MIMO) system with high order. Therefore, in this paper, we first establish a novel MIMO rotor dynamics closed-loop model and further convert it into an equivalent single-input and single-out (SISO) feedback control system by constructing complex variables, meanwhile reducing the system order by half. Beneficial from the equivalence between the MIMO and SISO systems, the sufficient and necessary conditions of the absolute stability of MLSM are derived by the extended inverse Nyquist stability criterion in the complex domain. Additionally, the effectiveness of the proposed modelling and stability analysis method is evaluated by simulation and experimental results. Thus, apart from PID parameters, this paper demonstrates that the stability of MLSM is also affected by the coupling of gyroscopic effect and non-collocated structure, which should serve as an essential guideline for system regulation of MLSM.

Design of an IMCPID Optimized Neural Network for Stepless Flow Control of Reciprocating Mechinery

It is usually difficult to design a controller for a nonlinear multiple-input and multiple-output (MIMO) system. The methodological approach taken in this study is a mixed methodology based on a PID-type internal model control (IMC) method and neural network (NN) optimization algorithm. The NN controller is designed for adjusting the sole parameter in IMCPID and compensating the characteristic changes and non-linearity in stepless flow control. In this study, a simulation of a nonlinear MIMO system with strong coupling is carried out. The simulation results indicate that the proposed control method has a better performance in settle time, overshoot, robustness and set-point tracking accuracy compared with other considered methods.

Adaptive Switching Hybrid Blast-STBC MIMO System

Fading in a wireless channel has negative effects on the performance of communication systems. Bell Laboratories layered space-time (BLAST) has been used to get a high data rate while space-time block codes (STBC) have been used to get a low bit error rate (BER) performance. Under deep faded channels, hybrid BLAST-STBC systems are considered as a trade-off between BLAST and STBC systems. By exploiting the benefits of both systems, a new method to represent a 4 × 4 multiple-input and multiple-output (MIMO) system is proposed and studied, in which the transmission process is carried out adaptively between both 4 × 4 VBLAST, Quasi-Orthogonal STBC (QOSTBC) and Hybrid systems according to the transmit links state. The proposed adaptive switching hybrid system (ASHS) reduces the total transmitted power, achieves the maximum throughput by obtaining the best BER. An adaptive switching transmission scheme using the strategy of measuring the transmit links fading is investigated as well. The simulated results are obtained in an environment of a 4 × 4 MIMO system using MATLAB platform where the total transmitting power is normalized to unity. The detections are done using the maximum likelihood (ML) receiver. The proposed ASHS system shows a lot of advantages such as maximum throughput is obtained in bad channel states, no additional transmit power is needed and no additional bandwidth is needed. Finally, under deep fading condition, the proposed ASHS transmission scheme obtains the best BER, reduces wasting the total transmitted power, achieves the maximum throughput and obtains the best BER.

Different‐factor compact‐form model‐free adaptive control with neural networks for MIMO nonlinear systems

AbstractIn this paper, a different‐factor structure‐based compact‐form model‐free adaptive control method with neural networks (DF‐CFMFAC‐NN) is proposed for a class of general multiple‐input and multiple‐output (MIMO) nonlinear systems. Its novelty lies in that it is a pure data‐driven control method using merely input/output data without any model information involved. Moreover, by virtue of the different‐factor structure, it addresses the problem in the prototype CFMFAC that mainly deals with a class of MIMO systems with similar channel characteristics under a fixed topology. Aiming at different characteristics between control channels, widely existing in MIMO systems, DF‐CFMFAC‐NN with learning parameters shows powerful tracking ability and flexible design for each control channel, and learning parameters are auto‐tuned by back propagation neural networks (BPNNs) online based on their self‐learning and self‐adapting properties. In the tuning process, a one‐step‐ahead partial derivative is directly derived by the dynamic linearization technique utilized in DF‐CFMFAC, which greatly improves the prediction accuracy of parameters. The convergence of tracking error and the stability of the tuning process are guaranteed by rigorous theoretical analysis. A coal mill system is provided to demonstrate its effectiveness and practicability, demonstrating it to be a promising control method for MIMO nonlinear systems.

An iterative detector based on sparse bayesian error recovery for uplink large-scale MIMO systems

In uplink large-scale multiple-input and multiple-output (MIMO) systems, the data detection at the receiver is the major challenge due to the considerable increase in dimensions of MIMO systems. In this paper, a Bayesian strategy is investigated for MIMO systems. The main idea of this algorithm is to improve the performance of a detector by finding the incorrectly detected symbols relying on the sparse property of the estimation error. In the proposed method, the conventional massive MIMO model is converted into a sparse model by inducing a sparse constrain on the symbol error vector obtained from a linear detector (minimum mean square error or zero forcing detectors). Then, by recovering the non-zero entries of error (i.e. the incorrectly detected symbols) via the sparse Bayesian approach, the primary estimate of the information vector is corrected. Numerical results reveal that exploiting the sparse characteristic of the estimation error leads to improvement in detection performance and the proposed Bayesian approach achieves better results than conventional and previous state of the art error recovery methods. Meanwhile, computational complexity of the sparse Bayesian error recovery (SBER) algorithm is the same as that of the linear detectors.

Low complexity FBMC for wireless MIMO systems

Filter Bank Multicarrier (FBMC) has become popular as an alternative to OFDM (Orthogonal Frequency Division Multiplexing). However, current FBMC systems found in the literature are not as compatible with MIMO (Multiple Input and Multiple Output) as OFDM is. This is due to the presence of inherent self-interference in current FBMC structures. Current literature uses maximal spectrally efficient waveforms, which have high interference among sub-carriers, thereby requiring complex post-processing in MIMO systems. In this work, we relax the spectral efficiency and show quantitatively that both the interference and out of band ripple energy can be decreased. We analyze this interference and its effects on different modulations at various SNR (Signal to Noise Ratio) values. We observe that the interference effect is more dominant at high SNR and affects higher order modulations more significantly. Reducing the interference enables near-orthogonal FBMC designs for practical SNR values and higher order modulations while still retaining MIMO receiver structures with low design complexity. We also show that error correcting codes can mitigate the effects of this interference. In this paper, first we compare our proposed FBMC system with OFDM and then with Filtered OFDM, one of the leading waveform candidates for 5G. Our simulations reveal that there is less than 1 dB gap between OFDM and FBMC having 84% and 97% spectral efficiencies respectively, using a 4 × 4 MIMO-VBLAST for 64-QAM at 30 dB SNR. Also, a comparison with Filtered OFDM system reveals that the proposed FBMC system is computationally more efficient for 5G systems.

Optimizing Process Recipe for Critical Dimensions in Dry Etching Process

Reducing variations of fabrication processes is a primary focus for improvement in etching process. One powerful tool employed towards this goal is Advanced Process Control (APC). APC adjusts recipes to compensate for measured variations. Systemic variations that were acceptable previously can become problematic as node sizes get smaller and structures become more complex. Dry etch can be a major source of variations and will be the focus of this research. Different APC methods have been developed to adjust recipes. However it is challenging to determine an optimal recipe to achieve multiple critical dimensions. In this article, an optimization method is proposed to generate optimal recipes for multiple input and multiple output (MIMO) systems. Models are constructed by applying Gaussian Process Regression and the optimal recipe is predicted using the proposed optimization method. Experimental data in dry etch process were collected and processed for model construction and recipe prediction. The results demonstrate the effectiveness of the proposed method in exploring optimal recipes for MIMO systems.

Hybrid Beamforming/Combining for Millimeter Wave MIMO: A Machine Learning Approach

Hybrid beamforming (HB) has emerged as a promising technology to support ultra high transmission capacity and with low complexity for Millimeter Wave (mmWave) multiple-input and multiple-output (MIMO) system. However, the design of digital and analog beamformer is a challenge task with non-convex optimization, especially for the multi-user scenario. Recently, the blooming of deep learning research provides a new vision for the signal processing of communication system. In this work, we propose a deep neural network based HB for the multi-User mmWave massive MIMO system, referred as DNHB. The HB system is formulated as an autoencoder neural network, which is trained in a style of end-to-end self-supervised learning. With the strong representation capability of deep neural network, the proposed DNHB exhibits superior performance than the traditional linear processing methods. According to the simulation results, DNHB outperforms about 2 dB in terms of bit error rate (BER) performance compared with existing methods.

Underwater cooperative MIMO communications using hybrid acoustic and magnetic induction technique

Future smart ocean applications require reliable communication networks connecting underwater sensors/robots and remote base stations, which is challenging to achieve due to the harsh and dynamic underwater channels. While Multiple-Input and Multiple-Output (MIMO) acoustic technique can address this problem, it is difficult to place multiple transducers on a single device due to the large wavelength. Although the cooperative MIMO technique could solve this issue, it was impossible to synchronize the distributed transmitters due to the extremely large and dynamic propagation delay of acoustic waves. In this paper, we propose an underwater cooperative MIMO communication mechanism, which is based on a hybrid acoustic and Magnetic Induction (MI) technique. The inter-node synchronization problem can be solved by using the MI technique so that the distributed transducers can cooperatively form narrow beams for long distance communications. The synchronization time and errors are significantly reduced since MI has negligible propagation delays. To quantitatively analyze the improvement, the closed-form of the synchronization error, signal-to-noise ratio (SNR), effective communication time, and throughput of the proposed system are rigorously derived. The proposed system is implemented in a software-defined testbed under the beamforming and space-time coding scheme. Through both numerical and experimental analysis, our hybrid cooperative MIMO mechanism can achieve much lower bit error rate and synchronization error than the conventional acoustic MIMO systems.

Automatic Modulation Classification for MIMO Systems via Deep Learning and Zero-Forcing Equalization

Automatic modulation classification (AMC) is one of the most critical technologies for non-cooperative communication systems. Recently, deep learning (DL) based AMC (DL-AMC) methods have attracted significant attention due to their preferable performance. However, the study of most of DL-AMC methods are concentrated in the single-input and single-output (SISO) systems, while there are only a few works on DL-based AMC methods in multiple-input and multiple-output (MIMO) systems. Therefore, we propose in this work a convolutional neural network (CNN) based zero-forcing (ZF) equalization AMC (CNN/ZF-AMC) method for MIMO systems. Simulation results demonstrate that the CNN/ZF-AMC method achieves better performance than the artificial neural network (ANN) with high order cumulants (HOC)-based AMC method under the condition of the perfect channel state information (CSI). Moreover, we also explore the impact of the imperfect CSI on the performance of the CNN/ZF-AMC method. Simulation results demonstrated that the classification performance is not only influenced by the imperfect CSI, but also associated with the number of the transmit and receive antennas.

Mutual Coupling Suppression in Antenna Arrays Using Meandered Open Stub Filtering Technique

Multiple-input and multiple-output (MIMO) systems utilize multiple antenna elements to improve channel capacity and achieve higher data rates. The successful implementation of MIMO systems requires low mutual coupling between antenna elements to reduce the envelope correlation coefficient (ECC) at the operating frequency. In this paper, we introduce a mutual coupling suppression technique using an open stub meandered (OSM) bandstop filter (BSF) design for MIMO applications. The OSM-BSF is optimized and implemented on the ground plane side of a two-element patch array. The designed filter provides high isolation of 60 dB at 2.4 GHz across a 30% bandwidth. A parametric study of antenna element spacing was carried out, showing at least 40 dB of mutual coupling suppression in the antenna array, irrespective of the antenna element spacing. A two-element antenna array prototype with OSM-BSF was fabricated and tested. Measurements showed that ≥ 57 dB isolation was achieved using OSM-BSF. Besides, the fabricated array provides a low ECC of 0.0093 at 2.42 GHz, while maintaining edge-to-edge spacing as little as $0.19\lambda _{0}$ .

Dynamic RF Chain Selection for Energy Efficient and Low Complexity Hybrid Beamforming in Millimeter Wave MIMO Systems

This paper proposes a novel architecture with a framework that dynamically activates the optimal number of radio frequency (RF) chains used to implement hybrid beamforming in a millimeter wave (mmWave) multiple-input and multiple-output (MIMO) system. We use fractional programming to solve an energy efficiency maximization problem and exploit the Dinkelbach method (DM)-based framework to optimize the number of active RF chains and data streams. This solution is updated dynamically based on the current channel conditions, where the analog/digital (A/D) hybrid precoder and combiner matrices at the transmitter and the receiver, respectively, are designed using a codebook-based fast approximation solution called gradient pursuit (GP). The GP algorithm shows less run time and complexity while compared to the state-of-the-art orthogonal matching pursuit (OMP) solution. The energy and spectral efficiency performance of the proposed framework is compared with the existing state-of-the-art solutions, such as the brute force (BF), the digital beamformer, and the analog beamformer. The codebook-free approaches to design the precoders and combiners, such as alternating direction method of multipliers (ADMMs) and singular value decomposition (SVD)-based solution are also shown to be incorporated into the proposed framework to achieve better energy efficiency performance.

Performance analysis of achievable sum‐rate for downlink massive MIMO systems

SummaryMassive multiple‐input and multiple‐output (MIMO) has been recognized as a promising technology in the fifth‐generation wireless networks. Under perfect channel state information, we derive three tractable closed‐form expressions that corresponding to the lower bound, approximation, and upper bound on the achievable rate in a massive MIMO downlink system with maximum‐ratio transmission precoding. Based on the proposed closed‐form expressions, the power efficiency of the system is investigated as the number of transmit antennas increases. Simulation results demonstrate the tightness of our proposed closed‐form expressions for the achievable sum‐rate.

BER Performance Evaluation of Turbo Coded MIMO System in Nakagami Channel

Wireless technologies are aiming to improve data rates along with reliability using Multiple Input and Multiple Output(MIMO) systems. The major performance parameter for advanced systems is Bit Error rate (BER). Researchers are working for minimizing the BER for data communication. This paper presents the BER performance of turbo coded Multiple Input Multiple Output (MIMO) system in Nakagami channel. MIMO system is realized using Space Time Block Codes. System performance is analyzed for M-ary Quadrature Amplitude Modulation (QAM) in Nakagami channel. System is implemented using MATLAB code. 4QAM system performs better as far as BER is concerned. The implemented turbo coded system outperforms the uncoded system in case of BER performance. This system can be used for improved performance of data communication in LTE and WiMax.

Efficient Turbo Inter Leaver Based MIMO OFDM System For Low Complexity Outdoor Communication

An arrangement of multiple antennas in both the receiver and transmitter called Multiple Input and Multiple Output (MIMO). The Orthogonal Frequency Division Multiplexing (OFDM) is enabled in MIMO system for high data wireless communications. Combination of both MIMO and OFDM Access (OFDMA) is a growing technology in next generation communication systems. In this work, the Bit Error Rate (BER) performance of MIMO-OFDMA is analyzed with Orthogonal Space Time Block Code (OSTBC), Maximum Ratio Combining (MRC) and Turbo coding scheme over flat fading channel are named as MIMO-MRC-OFDMA. OSTBC is a transmit diversity scheme, which is utilized for delivering an efficient transmission with high peak data rates that significantly improves the capacity of communication systems. Successively, the MRC diversity solves transmit and receive diversity from an OSTBC. MRC approach is applied in the receiving end for summing and weighing the received signals from the multiple paths. Besides, turbo coding scheme is utilized for error correction in a given code rate. The proposed system performance is evaluated in light of BER by varying the number of receive and transmit antennas such as 2×2, 2×4, 4×2 and 4×4.